Assessing Goodness-of-Fit in Marked-Point Process Models of Neural Population Coding via Time and Rate Rescaling
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Publication:3386424
DOI10.1101/2020.01.24.919050zbMath1453.92027OpenAlexW3087310269WikidataQ99569108 ScholiaQ99569108MaRDI QIDQ3386424
Yalda Amidi, Uri T. Eden, Behzad Nazari, Ali Yousefi
Publication date: 4 January 2021
Full work available at URL: https://doi.org/10.1162/neco_a_01321
Uses Software
Cites Work
- The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis
- Generalized Cramér-von Mises goodness-of-fit tests for multivariate distributions
- Multivariate generalizations of the Wald-Wolfowitz and Smirnov two-sample tests
- A multivariate Kolmogorov-Smirnov test of goodness of fit
- A common goodness-of-fit framework for neural population models using marked point process time-rescaling
- A comparison of uniformity tests
- Developments in Multiple Comparisons 1966-1976
- Testing multivariate uniformity and its applications
- A Bivariate Cramer-von Mises Type of Test for Spatial Randomness
- Testing multivariate uniformity: The distance‐to‐boundary method
- Clusterless Decoding of Position from Multiunit Activity Using a Marked Point Process Filter
- Testing randomness of spatial point patterns with the Ripley statistic
- Assessing Spatial Point Process Models Using Weighted K-functions: Analysis of California Earthquakes
- Integrability of Expected Increments of Point Processes and a Related Random Change of Scale
- THE PROBABILITY INTEGRAL TRANSFORMATION FOR TESTING GOODNESS OF FIT AND COMBINING INDEPENDENT TESTS OF SIGNIFICANCE
- Remarks on a Multivariate Transformation
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